


import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort




class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				# FIXME: add fast tests
				pass







@nightly
@require_onnxruntime
@require_torch_gpu
class       lowercase_					(unittest.TestCase   ):





				@property
				def 						__UpperCamelCase    (				self)  -> Dict:
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)





				@property
				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =ort.SessionOptions()
											a__      =False
											return options





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo.png')
											a__      =load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
											a__      =OnnxStableDiffusionInpaintPipeline.from_pretrained(
											    'runwayml/stable-diffusion-inpainting'       ,  revision='onnx'       ,  safety_checker=lowercase_       ,  feature_extractor=lowercase_       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
											pipe.set_progress_bar_config(disable=lowercase_)

											a__      ='A red cat sitting on a park bench'

											a__      =np.random.RandomState(0)
											a__      =pipe(
											    prompt=lowercase_       ,  image=lowercase_       ,  mask_image=lowercase_       ,  guidance_scale=7.5       ,  num_inference_steps=10       ,  generator=lowercase_       ,  output_type='np'       ,  )
											a__      =output.images
											a__      =images[0, 255:258, 255:258, -1]

											assert images.shape == (1, 512, 512, 3)
											a__      =np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo.png')
											a__      =load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
											a__      =LMSDiscreteScheduler.from_pretrained(
											    'runwayml/stable-diffusion-inpainting'       ,  subfolder='scheduler'       ,  revision='onnx')
											a__      =OnnxStableDiffusionInpaintPipeline.from_pretrained(
											    'runwayml/stable-diffusion-inpainting'       ,  revision='onnx'       ,  scheduler=lowercase_       ,  safety_checker=lowercase_       ,  feature_extractor=lowercase_       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
											pipe.set_progress_bar_config(disable=lowercase_)

											a__      ='A red cat sitting on a park bench'

											a__      =np.random.RandomState(0)
											a__      =pipe(
											    prompt=lowercase_       ,  image=lowercase_       ,  mask_image=lowercase_       ,  guidance_scale=7.5       ,  num_inference_steps=20       ,  generator=lowercase_       ,  output_type='np'       ,  )
											a__      =output.images
											a__      =images[0, 255:258, 255:258, -1]

											assert images.shape == (1, 512, 512, 3)
											a__      =np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3





def      UpperCAmelCase__		(UpperCamelCase_ = 10_00							):



     """simple docstring"""






     return sum(2 * a * ((a - 1) // 2) for a in range(3				,n + 1							)							)


if __name__ == "__main__":
 print(solution())




